Often, image stitching is used to combine multiple images with overlapping fields of view into a single image. The product of image stitching often has an increased field of view or increased resolution compared to an individual image that was used as a basis for creating the stitched image. Image stitching applications include panoramic images, image stabilization, maps created from satellite imagery, and the like. Image stitching often requires nearly exact overlaps between images to produce seamless results. Thus, automated methods and processes may be used to identify the overlaps between multiple images. The larger the number of images to be stitched, the greater the chance for error.
In “Automatic Panoramic Image Stitching Using Invariant Features,” (the Brown Article) the authors Matthew Brown and David G. Lowe propose processes to fully automate two-dimensional stitching, or multi-row stitching, of images to create a panoramic image. Feature-based matching methods, such as correlation of image patches around Harris corners, lack the invariance properties needed to enable reliable matching of arbitrary panoramic image sequences. Thus, the Brown article proposes correlating scale-invariant feature transform (SIFT) features between images using pairwise homographies based on the spherical coordinates of the locations of SIFT feature in two images. The homographies provide a relative rotation between the cameras used for the images and are used to stitch the images together. However, the stitched image does not accurately represent the panorama, as the homographies do not account for an unknown 3D rotation to a world coordinate frame. Thus, only after a global rotation is applied to the stitched images do the stitched images match to the captured scene.
However, the processes disclosed in the Brown Article with response to panoramic imagery may not translate well when applied to metallographs or stereoscopic images. Specifically, use of these processes disclosed in the article may introduce specific errors when applied to images of a planar surface captured by a camera having a narrow depth of focus. For example, using the spherical coordinate system to stitch together images of a flat surface may introduce inaccurate curving when stitching the images together. Further, while mapping stitched mages to a global coordinate system is necessary to straighten panoramic images, this additional step may unnecessarily complicate stitching of images of planar surfaces of the specimen, wasting both time and processing power. These and other shortcomings of the prior art are addressed by the present disclosure.